In many energy constrained networks, by making optimum use of the limited energy of nodes, early energy depletion may be avoided. One way in which this can be achieved is through clustering the nodes.
Generally, in a cluster, there is a significant difference in the amount of energy consumed by cluster heads and that consumed by other nodes. Because of this difference, most clustering approaches include mechanisms for balancing the nodes' workload to avoid premature death of certain nodes. To attain load balancing, most clustering approaches use the Round-Based Policy (RBP) approach which schedules the clustering-task statically by splitting the time into fixed length rounds, at the beginning of which clustering is performed. By rotating the cluster head responsibility among the nodes and reconstructing the cluster formation, periodic reclustering used by RBP balances the load of the network nodes. However, the load balancing function uses a significant amount of energy. When done repeatedly in each round, the reclustering operation can waste a lot of energy.
Additionally, clustering in fixed length rounds can only be used for networks with non-mobile nodes. For networks having mobile nodes (for example, in IoT applications), RBP-based clustering may not be effective, since the reclustering time is fixed/predetermined, while the need for reclustering depends on the mobility of the nodes and network topology changes. For example, if a node is removed from its cluster due to mobility, the network may need to recluster to place all the nodes in network clusters. However, according to the RBP, the node would need to continue its operation until the next round for reclustering.
Therefore, a need exists for providing an improved method of scheduling the clustering-tasks in networks that need consecutive reclustering.